{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.gray()\n",
    "import mglearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score:0.88\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X,y = make_blobs(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=1026)\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "\n",
    "print('Test set score:{:.2f}'.format(logreg.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores: [0.98 0.96 0.98]\n",
      "Cross validation mean score: 0.97\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "scores = cross_val_score(logreg, iris.data, iris.target,cv=3)\n",
    "print('Cross validation scores: {}'.format(scores))\n",
    "print('Cross validation mean score: {:.2f}'.format(scores.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_stratified_cross_validation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores: [0.96 0.96 0.98]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "kfold= KFold(n_splits=3,shuffle=True)\n",
    "skfold = StratifiedKFold(n_splits=5)\n",
    "print('Cross validation scores: {}'.format(cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cv iterations:  150\n",
      "Mean accuracy: 0.97\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
    "print(\"Number of cv iterations: \", len(scores))\n",
    "print(\"Mean accuracy: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_shuffle_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy: [0.97333333 0.98666667 0.97333333 0.96       0.98666667 0.96\n",
      " 0.97333333 0.92       0.97333333 0.96      ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "shuffle_split = ShuffleSplit(test_size=0.5, train_size=0.5, n_splits=10)\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)\n",
    "print(\"Mean accuracy: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[0.75       0.6        0.66666667]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "# create synthetic dataset\n",
    "X, y = make_blobs(n_samples=12, random_state=0)\n",
    "# assume the first three samples belong to the same group,\n",
    "# then the next four, etc.\n",
    "groups = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "scores = cross_val_score(logreg, X, y, groups=groups, cv=GroupKFold(n_splits=3))\n",
    "print(\"Cross-validation scores:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_group_kfold()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set:112  size of test set 38\n",
      "Best score:0.97\n",
      "Best parameters:{'C': 100, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "X_train,X_test,y_train,y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "print('Size of training set:{}  size of test set {}'.format(X_train.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        score = svm.score(X_test,y_test)\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {\"C\":C, \"gamma\":gamma}\n",
    "\n",
    "print('Best score:{:.2f}'.format(best_score))\n",
    "print('Best parameters:{}'.format(best_parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 84 size of validation set: 28 size of test set: 38\n",
      "Best score:0.96\n",
      "Best parameters:{'C': 10, 'gamma': 0.001}\n",
      "Test set score with best parameters: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "X_trainval,X_test,y_trainval,y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "X_train,X_valid,y_train,y_valid = train_test_split(X_trainval, y_trainval, random_state=1)\n",
    "\n",
    "print(\"Size of training set: {} size of validation set: {} size of test set: {}\".format(X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        score = svm.score(X_valid,y_valid)\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {\"C\":C, \"gamma\":gamma}\n",
    "\n",
    "print('Best score:{:.2f}'.format(best_score))\n",
    "print('Best parameters:{}'.format(best_parameters))\n",
    "\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval,y_trainval)\n",
    "test_score = svm.score(X_test,y_test)\n",
    "print(\"Test set score with best parameters: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 84 size of validation set: 28 size of test set: 38\n",
      "Best score:0.97\n",
      "Best parameters:{'C': 10, 'gamma': 0.1}\n",
      "Test set score with best parameters: 0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "X_trainval,X_test,y_trainval,y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
    "X_train,X_valid,y_train,y_valid = train_test_split(X_trainval, y_trainval, random_state=1)\n",
    "\n",
    "print(\"Size of training set: {} size of validation set: {} size of test set: {}\".format(X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        scores = cross_val_score(svm , X_trainval, y_trainval, cv=5)        \n",
    "        score = np.mean(scores)\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {\"C\":C, \"gamma\":gamma}\n",
    "\n",
    "print('Best score:{:.2f}'.format(best_score))\n",
    "print('Best parameters:{}'.format(best_parameters))\n",
    "\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval,y_trainval)\n",
    "test_score = svm.score(X_test,y_test)\n",
    "print(\"Test set score with best parameters: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x210 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_grid_search_overview()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "grid_search = GridSearchCV(SVC(), param_grid,cv = 5)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split( iris.data, iris.target, random_state=0)\n",
    "grid_search.fit(X_train, y_train)\n",
    "print(\"Test set score: {:.2f}\".format(grid_search.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# convert to DataFrame\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "# show the first 5 rows\n",
    "scores = np.array(results.mean_test_score).reshape(6, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x198039b5ae0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the mean cross-validation scores\n",
    "mglearn.tools.heatmap(scores, xlabel='gamma', xticklabels=param_grid['gamma'], ylabel='C', yticklabels=param_grid['C'], cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics and Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "y = digits.target==9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique predicted labels:[False].\n",
      "Test score: 0.90\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequest = dummy_majority.predict(X_test)\n",
    "print('Unique predicted labels:{}.'.format(np.unique(pred_most_frequest)))\n",
    "print('Test score: {:.2f}'.format(dummy_majority.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score:0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "pred_tree = tree.predict(X_test)\n",
    "print('Test score:{:.2f}'.format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dummy score: 0.90\n",
      "logreg score: 0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "dummy = DummyClassifier().fit(X_train, y_train)\n",
    "pred_dummy = dummy.predict(X_test)\n",
    "print(\"dummy score: {:.2f}\".format(dummy.score(X_test, y_test)))\n",
    "logreg = LogisticRegression(C=0.1).fit(X_train, y_train)\n",
    "pred_logreg = logreg.predict(X_test)\n",
    "print(\"logreg score: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not nine       0.99      1.00      0.99       403\n",
      "        nine       0.98      0.87      0.92        47\n",
      "\n",
      "    accuracy                           0.98       450\n",
      "   macro avg       0.98      0.93      0.96       450\n",
      "weighted avg       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_logreg, target_names=['not nine', 'nine']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_decision_threshold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.89      0.93       104\n",
      "           1       0.35      0.67      0.46         9\n",
      "\n",
      "    accuracy                           0.88       113\n",
      "   macro avg       0.66      0.78      0.70       113\n",
      "weighted avg       0.92      0.88      0.89       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from mglearn.datasets import make_blobs\n",
    "X, y = make_blobs(n_samples=(400, 50), cluster_std=[7.0, 2],random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=0.05,probability=True).fit(X_train, y_train)\n",
    "\n",
    "print(classification_report(y_test, svc.predict(X_test)))\n",
    "# print(classification_report(y_test, svc.decision_function(X_test)>-0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Recall')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "X,y=make_blobs(n_samples=(4000,500), cluster_std=[7.0,2], random_state=22)\n",
    "X_train, X_test, y_train ,y_test = train_test_split(X,y, random_state=0)\n",
    "svc = SVC(gamma=0.05).fit(X_train,y_train)\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, svc.decision_function(X_test))\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10, label='threshold zero', fillstyle='none', c='k', mew=2)\n",
    "plt.plot(precision, recall, label='precision recall curve')\n",
    "plt.xlabel('Precision')\n",
    "plt.ylabel('Recall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1980580fee0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=0, max_features=2)\n",
    "rf.fit(X_train, y_train)\n",
    "# RandomForestClassifier has predict_proba, but not decision_function\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "plt.plot(precision, recall, label=\"svc\")\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,label=\"threshold zero svc\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.plot(precision_rf, recall_rf, label=\"rf\")\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(precision_rf[close_default_rf], recall_rf[close_default_rf], '^', c='k', markersize=10, label=\"threshold 0.5 rf\", fillstyle=\"none\", mew=2)\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of random forest:0.610\n",
      "f1_score of svc:0.656\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print('f1_score of random forest:{:.3f}'.format(f1_score(y_test, rf.predict(X_test))))\n",
    "print('f1_score of svc:{:.3f}'.format(f1_score(y_test, svc.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision of random forest: 0.660\n",
      "Average precision of svc: 0.666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "ap_rf = average_precision_score(y_test, rf.predict_proba(X_test)[:,1])\n",
    "ap_svc= average_precision_score(y_test, svc.decision_function(X_test))\n",
    "print(\"Average precision of random forest: {:.3f}\".format(ap_rf))\n",
    "print(\"Average precision of svc: {:.3f}\".format(ap_svc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19805ac2e30>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresolds = roc_curve(y_test, svc.decision_function(X_test))\n",
    "\n",
    "plt.plot(fpr, tpr, label='ROC Curve')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR(recall)')\n",
    "close_zero = np.argmin(np.abs(thresolds))\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10, label='thresold zero', fillstyle='none', c='k', mew=2)\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19805a53970>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve SVC\")\n",
    "plt.plot(fpr_rf, tpr_rf, label=\"ROC Curve RF\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10, label=\"threshold zero SVC\", fillstyle=\"none\", c='k', mew=2)\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(fpr_rf[close_default_rf], tpr[close_default_rf], '^', markersize=10, label=\"threshold 0.5 RF\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for random forest:0.937\n",
      "AUC for SVC:0.916\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "rf_auc = roc_auc_score(y_test, rf.predict_proba(X_test)[:,1])\n",
    "svc_auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "print('AUC for random forest:{:.3f}'.format(rf_auc))\n",
    "print('AUC for SVC:{:.3f}'.format(svc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.951\n",
      "Confusion matrix:\n",
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 40  0  0  0  0  0  0  2  1]\n",
      " [ 0  1 40  3  0  0  0  0  0  0]\n",
      " [ 0  0  0 43  0  0  0  0  1  1]\n",
      " [ 0  0  0  0 37  0  0  1  0  0]\n",
      " [ 0  0  0  0  0 46  0  0  0  2]\n",
      " [ 0  1  0  0  0  0 51  0  0  0]\n",
      " [ 0  0  0  1  1  0  0 46  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  0  0  1  0  0  1 45]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\soft\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    " digits.data, digits.target, random_state=0)\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "pred = lr.predict(X_test)\n",
    "print(\"Accuracy: {:.3f}\".format(accuracy_score(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion_matrix(y_test, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_image = mglearn.tools.heatmap( confusion_matrix(y_test, pred), xlabel='Predicted label', ylabel='True label', xticklabels=digits.target_names,yticklabels=digits.target_names, cmap=plt.cm.gray_r, fmt=\"%d\")\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e21935a792cabaadabb39e077efde3aadff814271e124eae6fb74a8aae0f5081"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
